Towards behaviour-recognition-based video surveillance
Shaogang Gong · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
We present the latest results on learnable stochastic temporal models for automatic event and behaviour recognition in CCTV surveillance video. We introduce a novel approach to modelling and recognising complex activities involving simultaneous movement of multiple objects. Our approach differs from most previous work in that the visual understanding of activity is based on visual event detection and reasoning instead of object tracking and trajectory matching. Dynamic probabilistic graph models are exploited for modelling the temporal relationships among a set of different object temporal events. Typical applications of this technology include automatic semantic video content analysis, profiling and indexing of salient event and behaviour captured in CCTV video, and the early recognition of atypical behaviour in scenes where such behaviour could lead to a threat to safety.